A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability) (Hardcover)



Editorial Reviews
Product Description
Pattern recognition presents one of the most significant challenges for scientists and engineers and many different approaches have been proposed and developed. The aim of this book is to provide a self-contained and coherent account of probabilistic techniques which have been applied to the subject. Amongst the topics covered are: distance measures, kernel rules, nearest neighbor rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further help a reader's understanding. Research workers and graduate students will benefit from this wide-ranging and up-to-date account of this fast-moving field.



Product Details

• Hardcover: 660 pages
• Publisher: Springer; Corrected edition (April 4, 1996)
• Language: English
• ISBN-10: 0387946187
• ISBN-13: 978-0387946184

This review is from: A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability) (Hardcover)
In giving this book a second read, its importance finally dawned on me: it is one of the few if only books that provides a well-rounded theoretical (i.e. mathematical definitions and proofs) perspective on pattern recognition. Although other books, such as Duda et al's "Pattern Classification", have a significant degree of mathematical rigor, very few can claim to be based on the solid mathematical foundations of Lesbesgue measure theory, as this book is. This book has been a big inspiration for me, in that most pr papers I come across provide some method X, and show how experimentally it is more efficient or effective than methods Y and Z. Such papers, although possibly generating interest in the subject or method, do little if anything to advance the theory which in the end will have the final say of how, when, and why something works or when it doesn't. On the other hand, by making the assumption that the data comes from an unknown (i.e. nonparametric) probability measure space which induces an inherent optimal Bayesian error on the classification problem, this book shows how the theory of probability can be used to prove some very interesting results.
As an example, the authors define what it means to have a universally consistent classifier; i.e. a classifier which converges to the optimal Bayesian classifier as the amount of training data approaches infinity in the limit (irregardless of the data distribution). Moreover, one of the important results is that such classifiers exist and are often quite easy to devise (e.g. nearest-neighbor methods). And to be able to mathematically prove this is indeed inspiring. In closing, I would highly recommend this book to anyone who has the mathematical prereqs (probability from an abstract measure-theory point of view)
and is interested in doing high quality mathematical research in pattern recognition. For that audience this book will provide a good foundation for literally an unlimited number of interesting questions; many of which remain unanswered. For those who are more interested in the practice of pattern recogition, the above mentioned book by Duda et al. (ISBN 0471056693) will do just fine as a reference. The book "Pattern Recognition" by Theodoridis et al. is also of high quality (ISBN: 0126858756).


This review is from: A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability) (Hardcover)
This is an awesome book, the best in-depth book on statistical classification to date. Filled with theorems and proofs on classical nonparametric techniques plus neural networks and learning. Standard reference for anybody doing serious pattern recognition and learning. Destined to become a classical reference in the field.


This review is from: A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability) (Hardcover)
The book is great but the notations the authors employ will make you want to drop it on a first reading. Despite the generic title, it is really a reference book for the experts.
Issues in generalization are presented better in the book by Anthony and Bartlett but overall it is the best book available (for learning theorists).